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1. Structural network alterations in focal and generalized epilepsy assessed in a worldwide ENIGMA study follow axes of epilepsy risk gene expression

Author

Lariviere, S, Royer, J, Rodriguez-Cruces, R, Paquola, C, Caligiuri, ME, Gambardella, A, Concha, L, Keller, SS, Cendes, F, Yasuda, CL, Bonilha, L, Gleichgerrcht, E, Focke, NK, Domin, M, von Podewills, F, Langner, S, Rummel, C, Wiest, R, Martin, P, Kotikalapudi, R, O'Brien, TJ, Sinclair, B, Vivash, L, Desmond, PM, Lui, E, Vaudano, AE, Meletti, S, Tondelli, M, Alhusaini, S, Doherty, CP, Cavalleri, GL, Delanty, N, Kalviainen, R, Jackson, GD, Kowalczyk, M, Mascalchi, M, Semmelroch, M, Thomas, RH, Soltanian-Zadeh, H, Davoodi-Bojd, E, Zhang, J, Winston, GP, Griffin, A, Singh, A, Tiwari, VK, Kreilkamp, BAK, Lenge, M, Guerrini, R, Hamandi, K, Foley, S, Ruber, T, Weber, B, Depondt, C, Absil, J, Carr, SJA, Abela, E, Richardson, MP, Devinsky, O, Severino, M, Striano, P, Tortora, D, Kaestner, E, Hatton, SN, Vos, SB, Caciagli, L, Duncan, JS, Whelan, CD, Thompson, PM, Sisodiya, SM, Bernasconi, A, Labate, A, McDonald, CR, Bernasconi, N, Bernhardt, BC, Lariviere, S, Royer, J, Rodriguez-Cruces, R, Paquola, C, Caligiuri, ME, Gambardella, A, Concha, L, Keller, SS, Cendes, F, Yasuda, CL, Bonilha, L, Gleichgerrcht, E, Focke, NK, Domin, M, von Podewills, F, Langner, S, Rummel, C, Wiest, R, Martin, P, Kotikalapudi, R, O'Brien, TJ, Sinclair, B, Vivash, L, Desmond, PM, Lui, E, Vaudano, AE, Meletti, S, Tondelli, M, Alhusaini, S, Doherty, CP, Cavalleri, GL, Delanty, N, Kalviainen, R, Jackson, GD, Kowalczyk, M, Mascalchi, M, Semmelroch, M, Thomas, RH, Soltanian-Zadeh, H, Davoodi-Bojd, E, Zhang, J, Winston, GP, Griffin, A, Singh, A, Tiwari, VK, Kreilkamp, BAK, Lenge, M, Guerrini, R, Hamandi, K, Foley, S, Ruber, T, Weber, B, Depondt, C, Absil, J, Carr, SJA, Abela, E, Richardson, MP, Devinsky, O, Severino, M, Striano, P, Tortora, D, Kaestner, E, Hatton, SN, Vos, SB, Caciagli, L, Duncan, JS, Whelan, CD, Thompson, PM, Sisodiya, SM, Bernasconi, A, Labate, A, McDonald, CR, Bernasconi, N, and Bernhardt, BC

Abstract

Epilepsy is associated with genetic risk factors and cortico-subcortical network alterations, but associations between neurobiological mechanisms and macroscale connectomics remain unclear. This multisite ENIGMA-Epilepsy study examined whole-brain structural covariance networks in patients with epilepsy and related findings to postmortem epilepsy risk gene expression patterns. Brain network analysis included 578 adults with temporal lobe epilepsy (TLE), 288 adults with idiopathic generalized epilepsy (IGE), and 1328 healthy controls from 18 centres worldwide. Graph theoretical analysis of structural covariance networks revealed increased clustering and path length in orbitofrontal and temporal regions in TLE, suggesting a shift towards network regularization. Conversely, people with IGE showed decreased clustering and path length in fronto-temporo-parietal cortices, indicating a random network configuration. Syndrome-specific topological alterations reflected expression patterns of risk genes for hippocampal sclerosis in TLE and for generalized epilepsy in IGE. These imaging-transcriptomic signatures could potentially guide diagnosis or tailor therapeutic approaches to specific epilepsy syndromes.

Published
2022

2. Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study

Author

Gleichgerrcht, E, Munsell, BC, Alhusaini, S, Alvim, MKM, Bargallo, N, Bender, B, Bernasconi, A, Bernasconi, N, Bernhardt, B, Blackmon, K, Caligiuri, ME, Cendes, F, Concha, L, Desmond, PM, Devinsky, O, Doherty, CP, Domin, M, Duncan, JS, Focke, NK, Gambardella, A, Gong, B, Guerrini, R, Hatton, SN, Kalviainen, R, Keller, SS, Kochunov, P, Kotikalapudi, R, Kreilkamp, BAK, Labate, A, Langner, S, Lariviere, S, Lenge, M, Lui, E, Martin, P, Mascalchi, M, Meletti, S, O'Brien, TJ, Pardoe, HR, Pariente, JC, Rao, JX, Richardson, MP, Rodriguez-Cruces, R, Ruber, T, Sinclair, B, Soltanian-Zadeh, H, Stein, DJ, Striano, P, Taylor, PN, Thomas, RH, Vaudano, AE, Vivash, L, von Podewills, F, Vos, SB, Weber, B, Yao, Y, Yasuda, CL, Zhang, J, Thompson, PM, Sisodiya, SM, McDonald, CR, Bonilha, L, Gleichgerrcht, E, Munsell, BC, Alhusaini, S, Alvim, MKM, Bargallo, N, Bender, B, Bernasconi, A, Bernasconi, N, Bernhardt, B, Blackmon, K, Caligiuri, ME, Cendes, F, Concha, L, Desmond, PM, Devinsky, O, Doherty, CP, Domin, M, Duncan, JS, Focke, NK, Gambardella, A, Gong, B, Guerrini, R, Hatton, SN, Kalviainen, R, Keller, SS, Kochunov, P, Kotikalapudi, R, Kreilkamp, BAK, Labate, A, Langner, S, Lariviere, S, Lenge, M, Lui, E, Martin, P, Mascalchi, M, Meletti, S, O'Brien, TJ, Pardoe, HR, Pariente, JC, Rao, JX, Richardson, MP, Rodriguez-Cruces, R, Ruber, T, Sinclair, B, Soltanian-Zadeh, H, Stein, DJ, Striano, P, Taylor, PN, Thomas, RH, Vaudano, AE, Vivash, L, von Podewills, F, Vos, SB, Weber, B, Yao, Y, Yasuda, CL, Zhang, J, Thompson, PM, Sisodiya, SM, McDonald, CR, and Bonilha, L

Abstract

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.

Published
2021

3. Network-based atrophy modeling in the common epilepsies: A worldwide ENIGMA study

Author

Lariviere, S, Rodriguez-Cruces, R, Royer, J, Caligiuri, ME, Gambardella, A, Concha, L, Keller, SS, Cendes, F, Yasuda, C, Bonilha, L, Gleichgerrcht, E, Focke, NK, Domin, M, von Podewills, F, Langner, S, Rummel, C, Wiest, R, Martin, P, Kotikalapudi, R, O'Brien, TJ, Sinclair, B, Vivash, L, Desmond, PM, Alhusaini, S, Doherty, CP, Cavalleri, GL, Delanty, N, Kalviainen, R, Jackson, GD, Kowalczyk, M, Mascalchi, M, Semmelroch, M, Thomas, RH, Soltanian-Zadeh, H, Davoodi-Bojd, E, Zhang, J, Lenge, M, Guerrini, R, Bartolini, E, Hamandi, K, Foley, S, Weber, B, Depondt, C, Absil, J, Carr, SJA, Abela, E, Richardson, MP, Devinsky, O, Severino, M, Striano, P, Tortora, D, Hatton, SN, Vos, SB, Duncan, JS, Whelan, CD, Thompson, PM, Sisodiya, SM, Bernasconi, A, Labate, A, McDonald, CR, Bernasconi, N, Bernhardt, BC, Lariviere, S, Rodriguez-Cruces, R, Royer, J, Caligiuri, ME, Gambardella, A, Concha, L, Keller, SS, Cendes, F, Yasuda, C, Bonilha, L, Gleichgerrcht, E, Focke, NK, Domin, M, von Podewills, F, Langner, S, Rummel, C, Wiest, R, Martin, P, Kotikalapudi, R, O'Brien, TJ, Sinclair, B, Vivash, L, Desmond, PM, Alhusaini, S, Doherty, CP, Cavalleri, GL, Delanty, N, Kalviainen, R, Jackson, GD, Kowalczyk, M, Mascalchi, M, Semmelroch, M, Thomas, RH, Soltanian-Zadeh, H, Davoodi-Bojd, E, Zhang, J, Lenge, M, Guerrini, R, Bartolini, E, Hamandi, K, Foley, S, Weber, B, Depondt, C, Absil, J, Carr, SJA, Abela, E, Richardson, MP, Devinsky, O, Severino, M, Striano, P, Tortora, D, Hatton, SN, Vos, SB, Duncan, JS, Whelan, CD, Thompson, PM, Sisodiya, SM, Bernasconi, A, Labate, A, McDonald, CR, Bernasconi, N, and Bernhardt, BC

Abstract

Epilepsy is increasingly conceptualized as a network disorder. In this cross-sectional mega-analysis, we integrated neuroimaging and connectome analysis to identify network associations with atrophy patterns in 1021 adults with epilepsy compared to 1564 healthy controls from 19 international sites. In temporal lobe epilepsy, areas of atrophy colocalized with highly interconnected cortical hub regions, whereas idiopathic generalized epilepsy showed preferential subcortical hub involvement. These morphological abnormalities were anchored to the connectivity profiles of distinct disease epicenters, pointing to temporo-limbic cortices in temporal lobe epilepsy and fronto-central cortices in idiopathic generalized epilepsy. Negative effects of age on atrophy further revealed a strong influence of connectome architecture in temporal lobe, but not idiopathic generalized, epilepsy. Our findings were reproduced across individual sites and single patients and were robust across different analytical methods. Through worldwide collaboration in ENIGMA-Epilepsy, we provided deeper insights into the macroscale features that shape the pathophysiology of common epilepsies.

Published
2020

4. Structural network alterations in focal and generalized epilepsy assessed in a worldwide ENIGMA study follow axes of epilepsy risk gene expression.

Author

Larivière S, Royer J, Rodríguez-Cruces R, Paquola C, Caligiuri ME, Gambardella A, Concha L, Keller SS, Cendes F, Yasuda CL, Bonilha L, Gleichgerrcht E, Focke NK, Domin M, von Podewills F, Langner S, Rummel C, Wiest R, Martin P, Kotikalapudi R, O'Brien TJ, Sinclair B, Vivash L, Desmond PM, Lui E, Vaudano AE, Meletti S, Tondelli M, Alhusaini S, Doherty CP, Cavalleri GL, Delanty N, Kälviäinen R, Jackson GD, Kowalczyk M, Mascalchi M, Semmelroch M, Thomas RH, Soltanian-Zadeh H, Davoodi-Bojd E, Zhang J, Winston GP, Griffin A, Singh A, Tiwari VK, Kreilkamp BAK, Lenge M, Guerrini R, Hamandi K, Foley S, Rüber T, Weber B, Depondt C, Absil J, Carr SJA, Abela E, Richardson MP, Devinsky O, Severino M, Striano P, Tortora D, Kaestner E, Hatton SN, Vos SB, Caciagli L, Duncan JS, Whelan CD, Thompson PM, Sisodiya SM, Bernasconi A, Labate A, McDonald CR, Bernasconi N, and Bernhardt BC

Subjects
Adult, Gene Expression, Humans, Immunoglobulin E, Magnetic Resonance Imaging, Nerve Net, Connectome, Epilepsy, Epilepsy, Generalized genetics, Epilepsy, Temporal Lobe diagnosis, Epilepsy, Temporal Lobe genetics
Abstract

Epilepsy is associated with genetic risk factors and cortico-subcortical network alterations, but associations between neurobiological mechanisms and macroscale connectomics remain unclear. This multisite ENIGMA-Epilepsy study examined whole-brain structural covariance networks in patients with epilepsy and related findings to postmortem epilepsy risk gene expression patterns. Brain network analysis included 578 adults with temporal lobe epilepsy (TLE), 288 adults with idiopathic generalized epilepsy (IGE), and 1328 healthy controls from 18 centres worldwide. Graph theoretical analysis of structural covariance networks revealed increased clustering and path length in orbitofrontal and temporal regions in TLE, suggesting a shift towards network regularization. Conversely, people with IGE showed decreased clustering and path length in fronto-temporo-parietal cortices, indicating a random network configuration. Syndrome-specific topological alterations reflected expression patterns of risk genes for hippocampal sclerosis in TLE and for generalized epilepsy in IGE. These imaging-transcriptomic signatures could potentially guide diagnosis or tailor therapeutic approaches to specific epilepsy syndromes., (© 2022. The Author(s).)

Published
2022
Full Text
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5. Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study.

Author

Gleichgerrcht E, Munsell BC, Alhusaini S, Alvim MKM, Bargalló N, Bender B, Bernasconi A, Bernasconi N, Bernhardt B, Blackmon K, Caligiuri ME, Cendes F, Concha L, Desmond PM, Devinsky O, Doherty CP, Domin M, Duncan JS, Focke NK, Gambardella A, Gong B, Guerrini R, Hatton SN, Kälviäinen R, Keller SS, Kochunov P, Kotikalapudi R, Kreilkamp BAK, Labate A, Langner S, Larivière S, Lenge M, Lui E, Martin P, Mascalchi M, Meletti S, O'Brien TJ, Pardoe HR, Pariente JC, Xian Rao J, Richardson MP, Rodríguez-Cruces R, Rüber T, Sinclair B, Soltanian-Zadeh H, Stein DJ, Striano P, Taylor PN, Thomas RH, Elisabetta Vaudano A, Vivash L, von Podewills F, Vos SB, Weber B, Yao Y, Lin Yasuda C, Zhang J, Thompson PM, Sisodiya SM, McDonald CR, and Bonilha L

Subjects
Artificial Intelligence, Brain diagnostic imaging, Brain pathology, Hippocampus diagnostic imaging, Hippocampus pathology, Humans, Magnetic Resonance Imaging, Sclerosis pathology, Support Vector Machine, Epilepsy, Temporal Lobe diagnostic imaging, Epilepsy, Temporal Lobe pathology
Abstract

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Published
2021
Full Text
View/download PDF

6. Network-based atrophy modeling in the common epilepsies: A worldwide ENIGMA study.

Author

Larivière S, Rodríguez-Cruces R, Royer J, Caligiuri ME, Gambardella A, Concha L, Keller SS, Cendes F, Yasuda C, Bonilha L, Gleichgerrcht E, Focke NK, Domin M, von Podewills F, Langner S, Rummel C, Wiest R, Martin P, Kotikalapudi R, O'Brien TJ, Sinclair B, Vivash L, Desmond PM, Alhusaini S, Doherty CP, Cavalleri GL, Delanty N, Kälviäinen R, Jackson GD, Kowalczyk M, Mascalchi M, Semmelroch M, Thomas RH, Soltanian-Zadeh H, Davoodi-Bojd E, Zhang J, Lenge M, Guerrini R, Bartolini E, Hamandi K, Foley S, Weber B, Depondt C, Absil J, Carr SJA, Abela E, Richardson MP, Devinsky O, Severino M, Striano P, Tortora D, Hatton SN, Vos SB, Duncan JS, Whelan CD, Thompson PM, Sisodiya SM, Bernasconi A, Labate A, McDonald CR, Bernasconi N, and Bernhardt BC

Abstract

Epilepsy is increasingly conceptualized as a network disorder. In this cross-sectional mega-analysis, we integrated neuroimaging and connectome analysis to identify network associations with atrophy patterns in 1021 adults with epilepsy compared to 1564 healthy controls from 19 international sites. In temporal lobe epilepsy, areas of atrophy colocalized with highly interconnected cortical hub regions, whereas idiopathic generalized epilepsy showed preferential subcortical hub involvement. These morphological abnormalities were anchored to the connectivity profiles of distinct disease epicenters, pointing to temporo-limbic cortices in temporal lobe epilepsy and fronto-central cortices in idiopathic generalized epilepsy. Negative effects of age on atrophy further revealed a strong influence of connectome architecture in temporal lobe, but not idiopathic generalized, epilepsy. Our findings were reproduced across individual sites and single patients and were robust across different analytical methods. Through worldwide collaboration in ENIGMA-Epilepsy, we provided deeper insights into the macroscale features that shape the pathophysiology of common epilepsies., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

Published
2020
Full Text
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